Science AdvancesMicrobiologyPeer-reviewed journal article
Niche and local geography shape the pangenome of wastewater- and livestock-associated Enterobacteriaceae
L. P. Shaw, K. K. Chau, J. Kavanagh, M. AbuOun, E. Stubberfield, H. S. Gweon, L. Barker, G. Rodger, M. J. Bowes, A. T. M. Hubbard, H. Pickford, J. Swann, D. Gilson, R. P. Smith, S. J. Hoosdally, R. Sebra, H. Brett, T. E. A. Peto, M. J. Bailey, D. W. Crook, D. S. Read, M. F. Anjum, A. S. Walker, N. Stoesser, REHAB consortium
Abstract
Escherichia coli and other Enterobacteriaceae are highly diverse species with open pangenomes1,2, where genes move intra- and inter-species via horizontal gene transfer3. These species can cause clinical infections4,5 as well as persist environmentally6,7. Environmental populations have been suggested as important reservoirs of antimicrobial resistance (AMR) genes. However, as most analyses focus on clinical isolates8,9, the pangenome dynamics of natural populations remain understudied, particularly the role of plasmids. Here, we reconstructed near-complete genomes for 828 Enterobacteriaceae, including 553 Escherichia spp. and 275 non-Escherichia species with 2,293 circularised plasmids in total, collected from nineteen locations (livestock farms and wastewater treatment works in the United Kingdom) within a 30km radius at three timepoints over the course of a year. We find different dynamics for the chromosomal and plasmid-borne components of the pangenome, showing that plasmids have a higher burden of both AMR genes and insertion sequences, and AMR plasmids show evidence of being under stronger selective pressure. Focusing on E. coli, we observe that plasmid dynamics are more strongly dominated by niche and local geography, rather than phylogeny or season. Our results highlight the diversity of the AMR reservoir in these species and niches, and the importance of local strategies for controlling the emergence and spread of AMR.
- organism: bacteria (other)
- assay: metagenomics
- research focus: variant calling
- research focus: gene regulatory network inference
- approach: statistical model
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